I am interested in tools/techniques that can be used for analysis of streaming data in "real-time"*, where latency is an issue. The most common example of this is probably price data from a financial market, although it also occurs in other fields (e.g. finding trends on Twitter or in Google searches).
In my experience, the most common software category for this is "complex event processing". This includes commercial software such as Streambase and Aleri or open-source ones such as Esper or Telegraph (which was the basis for Truviso).
Many existing models are not suited to this kind of analysis because they're too computationally expensive. Are any models** specifically designed to deal with real-time data? What tools can be used for this?
* By "real-time", I mean "analysis on data as it is created". So I do not mean "data that has a time-based relevance" (as in this talk by Hilary Mason).
** By "model", I mean a mathematical abstraction that describe the behavior of an object of study (e.g. in terms of random variables and their associated probability distributions), either for description or forecasting. This could be a machine learning or statistical model.